Machine learning (ML) is often used to obtain control knowledge to improve planning efficiency. Usually, ML techniques are used in isolation from experience that could be obtained...
— We consider the problem of designing optimal distributed controllers whose impulse response has limited propagation speed. We introduce a state-space framework in which such co...
Adaptive query processing in large distributed systems has seen increasing importance due to the rising environmental fluctuations in a growing Internet. We describe Ginga, an ad...
Development of computerized embedded control systems is difficult because it brings together systems theory, electrical engineering and computer science. The engineering and analys...
Marcel Verhoef, Peter Visser, Jozef Hooman, Jan F....
We present an architecture that provides a robust, scalable and flexible software framework for planning and scheduling systems through the use of standardized industrial-strength...